Lactylation Gene Biomarkers in Acute Myeloid Leukemia

Acute myeloid leukemia research identifies seven lactylation-related hub genes (LSP1, MPO, GZMB, SPINK2, HLA-DRB1, HLA-DRA, POU2F2) as prognostic biomarkers. The study integrates data from GEO and TCGA databases, applying machine learning algorithms like LASSO-logistic and SVM-RFE for gene selection. It validates findings through Mendelian randomization, molecular docking, and experimental assays on AML cell lines.

Use Cases

• Validate the seven identified hub LRGs (LSP1, MPO, GZMB, SPINK2, HLA-DRB1, HLA-DRA, POU2F2) as prognostic markers using the provided gene expression and validation data.
• Apply the GLM model, identified as optimal in the study, to new AML patient data for survival prediction based on lactylation-related gene signatures.
• Screen for potential drugs targeting GZMB and LSP1 by replicating the molecular docking analysis described for compounds like (-)-gallocatechin gallate and benzo(a)pyrene.
• Analyze the relationship between immune cell infiltration and key LRGs using the CIBERSORT methodology outlined in the study.
• Assess pan-lactylation levels in AML cell lines under different treatments (exogenous lactate, sodium oxamate) following the experimental protocol described.

Strengths

• Identifies seven specific lactylation-related hub genes (LSP1, MPO, GZMB, SPINK2, HLA-DRB1, HLA-DRA, POU2F2) with detailed validation.
• Integrates data from two major public genomic databases, GEO and TCGA.
• Employs multiple machine learning algorithms (LASSO-logistic, SVM-RFE, Boruta) and validation models (DALEX package) for robust gene selection.
• Includes experimental validation via Western blotting, qRT–PCR, and immunohistochemistry on patient samples and cell lines.

Limitations

• The dataset is small at 338.2 KB, indicating limited raw data or summary results rather than a large primary dataset.
• Specific row and column counts for the underlying data tables are not provided, limiting assessment of analytical scope.
• Focus is on a specific biological mechanism (lactylation) in AML, which may limit generalizability to other cancers or conditions.

Provenance

Source

Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases.

Collection Method

Computational analysis (Seurat, WGCNA, machine learning algorithms) combined with experimental validation (Western blot, qRT–PCR, IHC).

Freshness

Last updated March 2026.